Fig. 1: Pipeline to generate vector embeddings for large-scale datasets that capture the morphological features of the neurons’ dendritic trees.

A Imaging of brain volume via electron microscopy and subsequent segmentation and tracing to render 3D meshes of individual neurons that are used for skeletonization. B Self-supervised learning of low-dimensional vector embeddings z₁, z₂ that capture the essence of the 3D morphology of individual neurons using GraphDINO. Two augmented “views” of the neuron are input into the network, where the weights of one encoder (bottom) are an exponential moving average (EMA) of the other encoder (top). The objective is to maximize the similarity between the vector embeddings of both views. Vector embeddings of similar neurons are close to each other in latent space. C An individual neuron is represented by its vector embedding as a point in the 32-dimensional vector space. D Quality control to remove neurons with tracing errors. Figure 1 was adapted from Weis, Hansel, Lüddecke, and Ecker, Self-Supervised Graph Representation Learning for Neuronal Morphologies, Transactions on Machine Learning Research, 899 (2023), https://openreview.net/pdf?id=ThhMzfrd6r under a CC BY license: https://creativecommons.org/licenses/by/4.0/.